Jo Schlemper

10.2k total citations · 3 hit papers
13 papers, 2.8k citations indexed

About

Jo Schlemper is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Jo Schlemper has authored 13 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Jo Schlemper's work include Advanced MRI Techniques and Applications (8 papers), Medical Imaging Techniques and Applications (7 papers) and Fetal and Pediatric Neurological Disorders (4 papers). Jo Schlemper is often cited by papers focused on Advanced MRI Techniques and Applications (8 papers), Medical Imaging Techniques and Applications (7 papers) and Fetal and Pediatric Neurological Disorders (4 papers). Jo Schlemper collaborates with scholars based in United Kingdom, United States and Germany. Jo Schlemper's co-authors include Daniel Rueckert, Anthony N. Price, Joseph V. Hajnal, José Caballero, Bernhard Kainz, Ozan Oktay, Ben Glocker, Michiel Schaap, Mattias P. Heinrich‬ and Chen Qin and has published in prestigious journals such as Magnetic Resonance in Medicine, IEEE Transactions on Medical Imaging and Medical Image Analysis.

In The Last Decade

Jo Schlemper

13 papers receiving 2.7k citations

Hit Papers

Attention gated networks: Learning to leverage salient re... 2017 2026 2020 2023 2019 2017 2018 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Attention gated networks: Learning to leverage salient regions in medical images
    2019 1.2k citations Jo Schlemper, Ozan Oktay et al. Medical Image Analysis profile →
  2. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
    2017 803 citations Jo Schlemper, José Caballero et al. IEEE Transactions on Medical Imaging profile →
  3. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
    2018 382 citations Chen Qin, Jo Schlemper et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jo Schlemper United Kingdom 10 1.8k 939 584 563 239 13 2.8k
Jian Yang China 31 1.5k 0.8× 1.5k 1.6× 945 1.6× 383 0.7× 127 0.5× 260 3.4k
Xujiong Ye United Kingdom 21 1.4k 0.8× 882 0.9× 416 0.7× 550 1.0× 196 0.8× 66 2.6k
Mattias P. Heinrich‬ Germany 26 1.9k 1.0× 1.9k 2.1× 820 1.4× 738 1.3× 78 0.3× 81 3.6k
Yuemin Zhu France 26 1.0k 0.6× 946 1.0× 311 0.5× 248 0.4× 176 0.7× 239 2.5k
Punam K. Saha United States 41 1.8k 1.0× 2.3k 2.5× 886 1.5× 403 0.7× 192 0.8× 207 5.8k
Sven Lončarić Croatia 27 755 0.4× 1.6k 1.7× 343 0.6× 239 0.4× 112 0.5× 233 3.1k
Julia A. Schnabel United Kingdom 31 2.6k 1.5× 2.1k 2.2× 1.0k 1.8× 673 1.2× 227 0.9× 188 4.8k
Heinz Handels Germany 29 1.3k 0.7× 1.2k 1.3× 786 1.3× 557 1.0× 134 0.6× 213 3.6k
Zeike A. Taylor United Kingdom 23 897 0.5× 694 0.7× 783 1.3× 277 0.5× 255 1.1× 73 2.5k
Ali Gholipour United States 31 1.6k 0.9× 986 1.1× 189 0.3× 742 1.3× 96 0.4× 136 3.8k

Countries citing papers authored by Jo Schlemper

Since Specialization
Citations

This map shows the geographic impact of Jo Schlemper's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jo Schlemper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jo Schlemper more than expected).

Fields of papers citing papers by Jo Schlemper

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jo Schlemper. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jo Schlemper. The network helps show where Jo Schlemper may publish in the future.

Co-authorship network of co-authors of Jo Schlemper

This figure shows the co-authorship network connecting the top 25 collaborators of Jo Schlemper. A scholar is included among the top collaborators of Jo Schlemper based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jo Schlemper. Jo Schlemper is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Krupinski, Elizabeth A., et al.. (2023). Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner. Journal of Medical Imaging. 10(S1). S11913–S11913. 3 indexed citations
2.
Zhou, Bo, Jo Schlemper, Seyed Sadegh Mohseni Salehi, et al.. (2023). DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 4955–4964. 43 indexed citations
3.
Schlemper, Jo, et al.. (2022). ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration. Lecture notes in computer science. 13436. 66–77. 11 indexed citations
4.
Zhou, Bo, Jo Schlemper, Seyed Sadegh Mohseni Salehi, et al.. (2022). Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction. Medical Image Analysis. 81. 102538–102538. 33 indexed citations
5.
Hammernik, Kerstin, Jo Schlemper, Chen Qin, et al.. (2021). Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination. Magnetic Resonance in Medicine. 86(4). 1859–1872. 51 indexed citations
6.
Qin, Chen, Jinming Duan, Kerstin Hammernik, et al.. (2021). Complementary time‐frequency domain networks for dynamic parallel MR image reconstruction. Magnetic Resonance in Medicine. 86(6). 3274–3291. 26 indexed citations
7.
Duan, Jinming, Ghalib Bello, Jo Schlemper, et al.. (2019). Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach. IEEE Transactions on Medical Imaging. 38(9). 2151–2164. 137 indexed citations
8.
Schlemper, Jo, Ozan Oktay, Michiel Schaap, et al.. (2019). Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis. 53. 197–207. 1249 indexed citations breakdown →
9.
Housden, R. James, Jacqueline Matthew, Daniel Rueckert, et al.. (2019). Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging. IEEE Transactions on Medical Imaging. 38(12). 2755–2767. 34 indexed citations
10.
Schlemper, Jo, Ozan Oktay, Liang Chen, et al.. (2018). Attention-Gated Networks for Improving Ultrasound Scan Plane Detection. 2 indexed citations
11.
Qin, Chen, Jo Schlemper, José Caballero, et al.. (2018). Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging. 38(1). 280–290. 382 indexed citations breakdown →
12.
Schlemper, Jo, José Caballero, Joseph V. Hajnal, Anthony N. Price, & Daniel Rueckert. (2017). A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging. 37(2). 491–503. 803 indexed citations breakdown →
13.
Nguyen, Duong-Van, et al.. (2010). Terrain classification based on structure for autonomous navigation in complex environments. c45. 163–168. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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